How can I use sigmoid layer at output for multilabel classification?
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layers = [ ...
sequenceInputLayer(11890)
bilstmLayer(100,'OutputMode','last')
fullyConnectedLayer(60)
sigmoidLayer
weightedClassificationLayer(classWeights)
]
I tried to use sigmoid activation function at output node for multilable classification, but it says "softmaxlayer is left out" whether classificationLayer is custom or not.
how to use sigmoid layer at output for classification?
2 Comments
Ankit Pasi
on 15 May 2021
Hi,
Did you end up solving your issue? I have a similar problem where the network graph does not accept sigmoid as the final layer and throws random errors. Useless actually compared to pytorch and tensorflow..
Answers (1)
Pratyush Roy
on 29 Sep 2020
The following link might be helpful:
sigmoidLayer has been introduced in MATLAB 2020b. The link to the documentation is given below:
1 Comment
Tobe freeman
on 19 Jun 2021
Thanks for these links, they were a useful step foward.
But I was not able to get too much further with them. The link to sigmoidLayer contains the following Tip:
"...To use the sigmoid layer for binary or multilabel classification problems, create a custom binary cross-entropy loss output layer or use a custom training loop.
>> Create a custom binary cross-entropy loss output layer or use a custom training loop
How? Also, keep in mind that if an expert provides you with a choice between two options they are likely signalling that neither of them actually work. But I digress.
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